CN113640672A - Battery state estimation using injected current oscillation - Google Patents

Battery state estimation using injected current oscillation Download PDF

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Publication number
CN113640672A
CN113640672A CN202110340404.5A CN202110340404A CN113640672A CN 113640672 A CN113640672 A CN 113640672A CN 202110340404 A CN202110340404 A CN 202110340404A CN 113640672 A CN113640672 A CN 113640672A
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current
battery pack
battery
controller
oscillation
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CN113640672B (en
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M·王
王跃云
H·夏
J·布内尔
C·W·万普勒
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GM Global Technology Operations LLC
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GM Global Technology Operations LLC
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/386—Arrangements for measuring battery or accumulator variables using test-loads
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/005—Detection of state of health [SOH]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4278—Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2220/00—Batteries for particular applications
    • H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/64—Electric machine technologies in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

A method for estimating a state of a battery pack using a controller having Battery State Estimator (BSE) logic includes receiving or delivering a constant baseline current via the battery pack. A current oscillation having a time-varying frequency content is selectively injected into the baseline current via a controller in response to a predetermined condition. The baseline current and the current oscillation are combined to form a final current. The method includes implementing battery parameters via BSE logic estimation concurrently with current oscillation to generate estimated battery parameters, and estimating, via a controller, a current state of the battery pack using the estimated battery parameters. The electrical system includes a rotating electrical machine electrically connected to and driven by the battery pack, and a controller configured to perform the method.

Description

Battery state estimation using injected current oscillation
Background
The present disclosure relates to real-time estimation of modeled battery parameters and battery states for a multi-cell battery pack. Accurate estimates allow an associated controller to effectively and efficiently control a variety of different power usage and utilization decisions during battery charge, steady state, and discharge modes of operation. Accordingly, the present disclosure is applicable to real-time control of electrified powertrain, powerplants, robots, mobile platforms, and other types of electrical systems where improved battery parameter and state estimation accuracy is desired.
In deterministic systems, ongoing measurements of multiple responses to a given input are not always possible or feasible, which in turn often requires the use of system models and response estimation based on such models. In a typical high energy battery, such as a lithium ion traction battery of an electric or hybrid electric vehicle, for example, voltage and temperature are periodically measured and estimated as a response to current. Different voltage states may be modeled, including equilibrium potentials, voltage responses based on hysteresis effects, voltage drops due to ohmic resistance, voltage drops due to battery dynamics, e.g., double layer and/or diffusion voltages, etc. Each of the exemplary voltage responses may be described in the model using an algebraic or differential function, or by using convolution integral. The voltage response described above affects, among other things, critical battery state estimates, such as state of charge (SOC) and state of power (SOP)/power capacity. Therefore, equivalent circuit models are often used in conjunction with adaptive Battery State Estimation (BSE) logic to estimate voltage response and other battery parameters.
As one of ordinary skill in the art will appreciate, a cell resting in an open circuit condition will eventually settle at an equilibrium voltage, referred to in the art as the Open Circuit Voltage (OCV) of the cell, if sufficient time is available. Ideally, the OCV of a given cell is unique for each SOC, independent of whether the cell is charged or discharged immediately prior to switching to an open circuit state, and independent of the magnitude of the battery current. Although OCV may be accurately determined in a battery pack that is in a closed state for a long duration, there are critical challenges when attempting to make an estimate of the battery state of an actively charging or discharging battery pack, particularly in a dynamically changing operating environment.
Particularly in a lithium ion battery, there is a nonlinear relationship between OCV and SOC. For example, in hybrid and battery electric vehicles, BSE logic in the form of a programmed algorithm may reference available OCV curves to help estimate SOC in real time. Alternatively, the SOC may be tracked over time from an initial SOC value using a process known in the art as Coulomb counting. Other BSE logic variants seek to balance voltage-based estimates and available coulomb count-based estimates in order to produce a composite estimate.
Disclosure of Invention
Methods and related systems are disclosed herein that aim to improve available battery parameter and state estimation accuracy in electrical systems having multi-cell battery packs. As part of the disclosed aspects, a controller is programmed to execute instructions embodying the present method, wherein the controller does so using Battery State Estimation (BSE) logic and current control logic as described herein. The controller uses a dedicated equivalent circuit model to accurately estimate and regress one or more relevant battery parameters. Representative, non-limiting regressive battery parameters, within the scope of the present disclosure, include Open Circuit Voltage (OCV), ohmic resistance (R-ohmic), and impedance of the battery pack, where SOC and SOP are representative battery states that may be estimated from such battery parameters using the disclosed methods.
As understood in the art, certain battery parameters enjoy greater predictive value than others during higher frequency current inputs, particularly when estimating the SOC and SOP/power capacity of the battery pack. Therefore, it is desirable to optimize estimation accuracy for such battery parameters. Ohmic resistance is one such parameter. Ohmic resistance, which is generally defined as the apparent internal resistance of the battery pack and the resistance of the various electrical conductors used in the construction of the battery pack. Ohmic resistance tends to manifest as a transient cell voltage response to changes in battery current, which is particularly significant for SOP/power capacity estimation.
It is recognized herein that as a basis for the present solution, a battery state estimator configured to regress battery parameters may include an Extended Kalman Filter (Extended Kalman Filter), a Sigma Point Kalman Filter (Sigma-Point Kalman Filter), a recursive least squares regression technique, etc., which may experience inadequate input signal variation/insufficient excitation levels under certain operating conditions. Insufficient excitation may in turn lead to inaccurate estimation results. The presence of noise in the signal measurement environment (such as measured current, voltage, and temperature) can result in a low signal-to-noise ratio. When insufficient frequency content is present in the input signal provided to the resident BSE logic, the predicted battery parameters may tend to drift, with the result that the battery parameters may rise or fall in a monotonic manner. Thus, the present solution aims to solve the problem by selectively modifying the constant baseline current (i.e. the charging or discharging current) of the battery pack by purposefully injecting a time-varying frequency content into the baseline current in the form of current oscillations.
In certain embodiments, a method for estimating a state of a battery pack via a controller having BSE logic configured to regress a set of battery parameters is provided. The method includes receiving or outputting a constant baseline current via the battery pack. The method also includes selectively requesting injection/addition of the time-varying frequency content to the constant baseline current in the form of a current oscillation, wherein such request occurs via the controller. This action is done in response to a predetermined condition which itself indicates that the frequency content is insufficient. The constant baseline current and the current oscillation combine to form a final current.
The method in this particular embodiment includes estimating, via the BSE logic, a battery parameter of the battery pack to thereby provide an estimated battery parameter, and thus estimating a current state of the battery pack using the estimated battery parameter as the estimated battery state.
The BSE logic may include an extended kalman filter or other kalman filter formulation.
Selectively requesting injection of the current oscillation into the constant baseline current may include requesting, via the controller, a constant charging current from the off-board charging station as the constant baseline current, and wherein controlling the power flow to or from the battery pack includes charging the battery pack using the final current. Alternatively, selectively requesting injection of the current oscillation into the constant baseline current comprises selectively controlling an on/off state of an electrical load connected to the battery pack while receiving or delivering the constant baseline current to thereby generate the current oscillation. Controlling power flow to or from the battery pack in this case may include discharging the battery pack to an electrical load.
As another alternative, selectively requesting injection of the current oscillation into the constant baseline current may include selectively requesting a series of constant charging currents from the offboard charging station, each constant charging current having a different frequency content to thereby produce the current oscillation, and wherein controlling power flow to or from the battery pack using the estimated battery state includes charging the battery pack using the final current, or communicating a charging request from the controller to the offboard smart charger. Such a smart charger may be configured to detect a demand for the final current by the battery pack and to deliver the final current to the battery pack as a charging current.
In various embodiments, the battery parameters may include an estimated ohmic resistance, impedance, and/or open circuit voltage of the battery pack.
In an exemplary embodiment, the frequency of the current oscillation may be less than about 1Hz, and the constant baseline current may have a frequency less than about 0.01 Hz. The current oscillation may comprise a pseudo-random binary signal having a time-varying frequency, or a pulse width modulated or pulse density modulated signal having a time-varying frequency, or a sequence of chirped signals.
In possible embodiments, the predetermined condition may include a threshold covariance or estimated error value from the BSE logic, or a duration in which the constant baseline current remains constant prior to current oscillation injection.
Also disclosed herein is an electrical system that, according to an exemplary embodiment, includes a rotating electrical machine electrically connected to and driven by a battery pack, and a controller configured to estimate a current state of the battery pack using the BSE logic described above. In one exemplary embodiment, the controller is configured to determine, via the BSE logic, a frequency content of a constant baseline current delivered to or from the battery pack, wherein the constant baseline current has a frequency of less than about 0.01 Hz. The controller is further configured to selectively request injection of a current oscillation into the constant baseline current in response to a predetermined condition, wherein the constant baseline current and the current oscillation combine to form a final current. In a non-limiting embodiment, the current oscillation has a frequency in a range between about 0.1Hz and 1Hz, such as within ± 5% or ± 10% of the value or within a reasonable tolerance thereof, and to estimate battery parameters of the battery pack via BSE logic concurrently with the current oscillation to thereby generate estimated battery parameters. In this embodiment, the estimated battery parameter is the ohmic resistance, impedance, and/or open circuit voltage of the battery pack.
The controller is further configured to estimate a current state of the battery pack using the estimated battery parameters as the estimated battery state, and to thereby control power flow from the motor to the battery pack or from the battery pack to the motor, respectively, using the estimated battery state. One or more wheels may be connected to the rotary motor.
The above summary is not intended to represent each possible embodiment or every aspect of the present disclosure. Rather, the foregoing summary is intended to illustrate some of the novel aspects and features disclosed herein. The above features and advantages and other features and advantages of the present disclosure will be readily apparent from the following detailed description of representative embodiments and modes for carrying out the present disclosure when taken in connection with the accompanying drawings and appended claims.
The invention also provides the following technical scheme:
1. a method for estimating a current state of a battery pack using a controller having Battery State Estimator (BSE) logic, the method comprising:
receiving or delivering a constant baseline current via the battery pack;
selectively requesting, via the controller, injection of a current oscillation having a time-varying frequency content into the constant baseline current in response to a predetermined condition, wherein the constant baseline current and the current oscillation combine to form a final current;
estimating battery parameters of the battery pack via the BSE logic concurrently with the current oscillation to thereby generate estimated battery parameters; and is
Estimating, via the controller, the current state of the battery pack using the estimated battery parameters to thereby generate an estimated battery state of the battery pack.
2. The method of scheme 1, wherein the BSE logic includes a kalman filter.
3. The method of scheme 1, further comprising: controlling, via the controller, power flow to or from the battery pack using the estimated battery state.
4. The method of claim 3, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises requesting, via the controller, a constant charging current from an off-board charging station as a constant baseline current; and wherein controlling power flow to or from the battery pack comprises charging the battery pack using the final current.
5. The method of claim 3, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises selectively controlling an on/off state of an electrical load connected to the battery pack to thereby generate the current oscillation while receiving or delivering the constant baseline current, and wherein controlling power flow to or from the battery pack comprises discharging the battery pack to the electrical load.
6. The method of claim 3, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises selectively requesting a series of constant charging currents from an offboard charging station, each of the constant charging currents having a different frequency content, to thereby generate the current oscillation, and wherein controlling power flow to or from the battery pack using the estimated battery state comprises charging the battery pack using the final current.
7. The method of claim 1, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises communicating a charge request from the controller to an off-board smart charger configured to detect a demand of the battery pack for the final current and to communicate the final current as a charge current to the battery pack.
8. The method of claim 1, wherein estimating the battery parameter comprises estimating an ohmic resistance, an impedance, and/or an open circuit voltage of the battery pack.
9. The method of scheme 1, wherein the frequency of the current oscillation is less than about 1Hz and the constant baseline current has a frequency less than about 0.01 Hz.
10. The method of scheme 1, wherein the current oscillation comprises a pseudo-random binary signal having a time-varying frequency.
11. The method of scheme 1, wherein the current oscillation comprises a pulse width modulated signal or a pulse density modulated signal having a time varying frequency.
12. The method of scheme 1, wherein the current oscillation is a chirp signal sequence.
13. The method of scheme 1, wherein the predetermined condition comprises a threshold covariance or estimated error value from the BSE logic.
14. The method of scheme 1, wherein the predetermined condition comprises a calibration duration in which the constant baseline current is held constant prior to the current oscillation injection.
15. An electrical system, comprising:
a battery pack;
a rotating electrical machine electrically connected to and driven by the battery pack; and
a controller configured to estimate a current state of a battery pack using Battery State Estimator (BSE) logic, wherein the controller is configured to:
determining, via the BSE logic, a frequency content of a constant baseline current delivered to or from the battery pack, wherein the constant baseline current has a frequency of less than about 0.01 Hz;
selectively requesting injection of a current oscillation into the constant baseline current in response to a predetermined condition, wherein the constant baseline current and the current oscillation combine to form a final current, and wherein the current oscillation has a frequency in a range between about 0.1Hz and 1 Hz;
estimating battery parameters of the battery pack via the BSE logic concurrently with the current oscillation to thereby generate estimated battery parameters, wherein the estimated battery parameters are an ohmic resistance, an impedance, and/or an open circuit voltage of the battery pack;
estimating the current state of the battery pack using the estimated battery parameters as estimated battery states; and is
Using the estimated battery state to control power flow from the electric machine to the battery pack or from the battery pack to the electric machine, respectively.
16. The electrical system of claim 15, wherein the BSE logic comprises a kalman filter.
17. The electrical system of claim 16, wherein the predetermined condition comprises a covariance value, and wherein the estimated battery parameter is a regressive ohmic resistance of the battery pack.
18. The electrical system of claim 15, wherein the controller is configured to selectively request injection of the current oscillation into the constant baseline current by requesting a constant charging current as a constant baseline current from an offboard charging station, and to control charging of the battery pack using the final current as a power flow.
19. The electrical system of claim 15, wherein the current oscillation comprises a pseudo-random binary signal, a pulse width modulated signal, a pulse density modulated signal, and/or a sequence of chirp signals.
20. The electrical system of claim 15, further comprising one or more wheels connected to the rotating electrical machine.
Drawings
Fig. 1 is a schematic diagram of an example electrical system having a battery pack and a controller including Battery State Estimation (BSE) logic configured to regress battery parameters and estimate a state of the battery pack.
Figure 2 is a time plot of regressive ohmic resistance (vertical axis) versus time (horizontal axis) without the present teachings.
Fig. 3A and 3B are time plots of regressive ohmic resistance and percentage of state-of-charge plotted on the respective vertical axes and time plotted on the respective horizontal axes over a representative charging cycle.
Fig. 4A and 4B are time plots of regressive ohmic resistance and percentage of state of charge plotted on the respective vertical axes and time plotted on the respective horizontal axes during a representative discharge cycle.
Fig. 5 is a schematic flow diagram depicting charge control logic that may be used by the controller depicted in fig. 1 when selectively injecting frequency content into a constant baseline current according to the present teachings, with nominal current oscillations depicted on the vertical axis and time depicted on the horizontal axis.
Fig. 6 is a time diagram depicting a representative final current on the vertical axis and time on the horizontal axis.
Fig. 7 is a time diagram showing a representative application of the present method, in which the magnitude of the final current is plotted on the vertical axis and the time is plotted on the horizontal axis.
The present disclosure is susceptible to modifications and alternative forms, wherein representative embodiments are shown by way of example in the drawings and will be described in detail below. The inventive aspects of the present disclosure are not limited to the specific forms disclosed. On the contrary, the present disclosure is intended to cover modifications, equivalents, combinations, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
Detailed Description
Reference is made to the drawings wherein like reference numerals refer to like elements. FIG. 1 depicts an exemplary vehicle 10 having an on-board electrical system 12, a controller (C) 50, and a set of wheels 11, where the wheels 11 are in rolling contact with a road surface 20. Vehicle 10 illustrates only one possible application of the present teachings and is used herein for illustrative consistency purposes only. Those of ordinary skill in the art will appreciate that the present teachings extend to a wide variety of mobile systems and devices, such as, but not limited to, motor vehicles, watercraft, aircraft, rail vehicles, mobile platforms, robots, power plants, or other systems having similar electrical systems 12.
The electrical system 12 in the non-limiting embodiment of FIG. 1 includes a high energy/high voltage multi-cell battery pack 13 (B)HV) Its various battery parameters and states are estimated by the controller 50, as described herein. By way of example and not limitation, the battery pack 13 may have lithium ion battery chemistry and may be capable of outputting at least 18V and up to 400V or higher depending on the configuration.
In some embodiments of vehicle 10, electrical system 12 includes a multi-phase rotating electrical machine (M)E) 15 such as a motor-generator unit. In such an embodiment, the motor torque (arrow T) from the energized electric machine 15M) May be transmitted to one or more of the wheels 11 and/or to other coupled loads. A Power Inverter Module (PIM) 17 is disposed between the battery pack 13 and the electric machine 15 and is configured to invert the DC Voltage (VDC) from the battery pack 13 and thereby generate a multi-phase/AC Voltage (VAC) for energizing stator windings (not shown) of the electric machine 15 in response to pulse width modulation or other suitable high speed switching control signals and operation of phase-dependent semiconductor switches (not shown). Similarly, operation of PIM17 may convert AC Voltage (VAC) from motor 15 to DC Voltage (VDC) suitable for recharging battery pack 13.
The battery pack 13, generally referred to above, includes a plurality of electrochemical cells 14. For improved clarity and simplicity, four such cells 14 are labeled in fig. 1 as C1, C2, C3, and C4, respectively. The actual number of battery cells 14 used in the construction of the battery pack 13 is application specific and depends on the energy requirements of the electrical load or device being powered by the battery pack 13, such as, but not limited to, the rotating electrical machine 15. Although shown schematically for simplicity and clarity of illustration, the electric machine 15 may be coupled to the wheels 11, either directly or via an intermediate gear arrangement and a drive shaft, to power the electric machine 15 within the capacity of the electric machine 15 as an electric traction motor and thereby propel the vehicle along the roadway 20.
Power flow to or from electrical system 12 may be managed in real time by controller 50, for example, when controller 50 is configured as a battery system manager or other control device or devices, wherein controller 50 outputs a control signal (arrow CC) via an output control signal (arrow CC)O) Regulating ongoing operation of the electrical system 12. In accordance with the present strategy, the controller 50 employs Battery State Estimation (BSE) logic 52, a dedicated equivalent circuit model (K-EQ) 54, and sensors 16 that collectively measure and communicate input signals to the controller 50 and its resident BSE logic 52. In the illustrated configuration such input signals include cell voltages (arrow V)C) Battery current (arrow I), and battery temperature (arrow T). In different embodiments, the input signal may be determined locally within each battery cell 14, or may be measured collectively at the level of the battery pack 13 and back-calculated or estimated from such level.
The controller 50 may be configured as part of a larger battery management system or as a separate computer device or network of such devices, the controller 50 including a processor (P), e.g., a microprocessor or central processing unit; a memory (M) in the form of a read only memory, a random access memory, an electrically programmable read only memory or the like; a high-speed clock; analog-to-digital and digital-to-analog circuits; input/output circuits and devices; and appropriate signal conditioning and buffering circuitry. The policies described below may be encoded as machine-readable instructions, which are collectively referred to herein as method 100.
In carrying out the present method 100, the controller 50 automatically derives the current operating state of the battery, including the bulk charge state and power state of the battery pack 13. The controller 50 uses the BSE logic 52 to do this with the aid of an equivalent circuit model 54, which equivalent circuit model 54 typically models the behavior of the battery pack 13 using battery voltage, hysteretic voltage source, ohmic resistance, battery and/or cell voltage, resistance, and capacitance, etc. as circuit elements, and taking into account factors such as surface charge on the various cells 14. Depending on the complexity of the equivalent circuit model 54, the equivalent circuit model 54 may also take into account solid state diffusion voltage effects occurring in the constituent cells 14 of the battery pack 13 as well as other higher and/or lower frequency voltage effects. Collectively, various voltage effects are added or subtracted from the open circuit voltage of the battery cell 14.
The particular configuration of the equivalent circuit model 54 is based on the particular application and configuration of the battery pack 13, and thus may have a wide variety of configurations. Non-limiting representative example constructs that may be used as equivalent circuit model 54 may be found in the following documents: for example, U.S. patent No. 9,575,128 entitled "Battery State-Of-Charge Estimation For Hybrid and Electric Vehicles Using Extended Kalman Filter technologies" issued on 21.2.2017, U.S. patent No. 6,639,385 entitled "State Of Charge Method and Apparatus" issued on 28.10.2003, and U.S. patent No. 7,324,902 entitled "Method and Apparatus For Generalized Recurved lens Process For Battery State Of Charge and State Of Health" issued on 29.1.2008, the entire contents Of which are hereby incorporated by reference.
The state of charge and power state estimates are adjusted in real time using BSE logic 52. In a possible embodiment, the BSE logic 52 may include an extended kalman filter and additional current control logic 55 (OSC), where an example of the current control logic 55 is depicted in fig. 5 to improve overall estimation accuracy in the face of a constant baseline current flowing into or out of the battery pack 13 of fig. 1. As will be appreciated by those of ordinary skill in the art, the extended kalman filter formula is typically used to process a system model having the following general form:
Figure 598522DEST_PATH_IMAGE001
wherein,w k andn k is the noise factor. For the representative BSE logic 54 of the present disclosure, the inputs areu k =i k = current to or from battery pack 13. Measured value isz k =V k Which in this case is the cell voltage of the battery cell 14 or the pack voltage of the battery pack 13, which are schematically shown in fig. 1.x k Is a state vector that includes the battery parameters to be estimated by the BSE logic 52.
As understood in the art, the estimated state of the battery pack 13 and other deterministic systems is the smallest vector that summarizes the collective past of the systems. Within the scope of the present disclosure, alternatives to the extended kalman filter formulation include, but are not limited to, Sigma point kalman filters, and the like, as well as formulations that do not follow the form of a kalman filter, e.g., recursive least squares regression, particle filters, and the like. An extended kalman filter that effectively uses the single points and partial derivatives of the associated equivalent circuit model 54 is therefore one possible method of regressing the battery parameters within the scope of this disclosure.
Still referring to fig. 1, the present solution implemented by the controller 50 and its resident BSE logic 52 is intended to operate in an electrical system that typically has a constant baseline current, such as the exemplary electrical system 12 and battery pack 13. The baseline current set forth herein may be from an off-board charging station (V)CH) 25, which non-on-board charging station 25 is connectable to the vehicle 10 via, for example, the charging port 10C, to start a charging cycle of the battery pack 13. The charging station 25 may deliver AC or DC charging current according to the configuration of the non-vehicle charging station 25; or the baseline current may be a battery current supplied by the battery pack 13 to power the motor 15, resistive elements, and/or other electrical loads. In some embodiments, the charging station 25 may be adapted to function as a smart charger 25S, and thus be equipped with associated processors, logic, sensors, and for communicating with the controller 50 to determine the charging requirements of the battery pack 13Other required hardware and software.
As used herein, the term "constant" with respect to a baseline current refers to a current having a very low frequency content, e.g., in various embodiments, less than about 0.01Hz or less than about 0.005 Hz. The term "very low" is understood to refer to the sampling rate of the controller 50 when the BSE logic 52 is implemented. In an exemplary embodiment, such a sampling rate may be less than about 1-10 Hz. Since the off-board charging station 25 may optionally be implemented as a DC fast charger capable of fast charging the battery pack 13 via a DC charging voltage and associated DC charging current, the DC current waveform concentration embodies constancy within the scope of the present disclosure, and thus the constant baseline current processed herein may be a DC charging current or an Alternating Current (AC) charging current having a very low frequency content as defined above.
As described above, the controller 50 of fig. 1 is configured for estimating the battery parameters and the current state of the battery pack 13 using the BSE logic 52. In one embodiment, the method 100 includes receiving or delivering a constant baseline current from or to the load, respectively, via the battery pack 13. As described below with reference to fig. 2-7, the method 100 includes selectively requesting injection of a time-varying frequency content as a current oscillation (e.g., a dither signal) into a constant baseline current. This occurs through operation of controller 50 using current control logic 55.
With respect to current control logic 55, and referring briefly to FIG. 5, the baseline current (i)BL) 42 and current oscillation (i)OSC) 44 (shown as varying within nominal/representative 3A) are added to combine and form the final current (i)F)46. The non-limiting exemplary embodiment of fig. 5 depicts the current oscillation 44 as a Pseudo-Random Binary Signal (Pseudo-Random Binary Signal) or PRBS oscillation. There are alternative embodiments of the current oscillation 44, including a frequency variable signal (such as a pulse width modulated or pulse density modulated signal), a sequence of chirped signals, or other frequency variable signals configured to generate sufficient excitation to the BSE logic 52. Although the frequency of the current oscillation 44 may vary with the application or in a given implementation, the frequency content should be relative to a constant basisThe line current is high, e.g., in the range of about 0.1-1Hz, or anywhere within such a range, e.g., a discrete frequency of 0.1Hz, 0.5Hz, or 1 Hz.
According to the present method 100, battery parameters (such as regressive R-ohms, capacitance, or OCV) of the battery pack 13 are automatically estimated via the BSE logic 52 of fig. 1 and concurrently with the injection of the current oscillation 44, where "injection" as used herein refers to current oscillation (i) asOSC) 44 are combined with or superimposed on the summation of the constant baseline currents 42, respectively, as indicated by the summing node (+). Temporarily, the resulting waveform, i.e. the final current (i)F) 46, provided to or by the battery pack 13, an exemplary embodiment of the final current 46 for a representative time period of t(s) =600 seconds is shown in fig. 6. The controller 50 of fig. 1 may then estimate the current state of the battery pack 13 using the estimated battery parameters.
Referring to fig. 2, a potential vulnerability of the BSE logic 52 of fig. 1 in the absence of the present teachings and the use of the current control logic 55 of fig. 5 is a possible degradation in estimation accuracy due to the lack of sufficient frequency content in the constant baseline current 42. The constant baseline current 42, shown as a step signal for simplicity in fig. 2 and labeled i (a) on the vertical axis, is an example of the very low frequency content described above. A representative battery parameter that may be estimated by the BSE logic 52 is the regressive ohmic resistance/R-ohm (trace 30), which is abbreviated as R- Ω and is shown on the other vertical axis of fig. 2. In the illustrated trace 30, the regressive ohmic resistance increases monotonically. Estimating the differences that exist between the battery parameters may reduce the overall accuracy of SOC and SOP estimation when charging the battery pack 13 of fig. 1 using a constant charging current and when discharging the battery pack 13 during a typical drive cycle, with possible results of such accuracy degradation indicated in fig. 2 as monotonically increasing ohmic resistance.
The potential vulnerability in the form of sub-optimal estimation accuracy may be better understood with reference to fig. 3A, 3B, 4A, and 4B. Trace 130 of fig. 3A is similar to that of fig. 2 in that it depicts a monotonically increasing R-ohm value of the type that may result from a continuously constant charging current (e.g., from the off-board charging station 25 shown in fig. 1). In fig. 3B, for proper calibration of the controller 50 and the resident BSE logic 52, the difference between trace "soc.v" 35 (i.e., the SOC% regressed by the BSE logic 52) and trace 34 "soc.ahr" providing the self coulomb counting method should ideally be minimal, e.g., less than 5%. In practice, as indicated by the variation between traces 34 and 35 in fig. 3B, it is difficult to adjust the onboard calibration under constant current charging conditions to accurately meet this demand. Indeed, after a constant charging current is sustained, R-ohm and other estimated battery parameters may take a longer amount of time to return to the normal or expected range over a subsequent drive period, e.g., up to 20 minutes or more than one hour, depending on the initial return R-ohm value at the beginning of the drive period.
Fig. 4A and 4B illustrate the effect on an exemplary R-ohm battery parameter in the face of a non-constant battery current, where fig. 4A and 4B represent the response in the regressive R-ohm from the BSE logic 52 when the battery pack 13 is discharged during the exemplary drive/discharge cycle of the vehicle 10 shown in fig. 1. Although the battery current during a discharging event, such as a drive cycle of the vehicle 10, is generally non-constant, sometimes the vehicle 10 may cruise at a fixed speed for a longer duration due to, for example, rapid changes in the output torque request. The observed effect on the estimated or regressive R-ohm (i.e., trace 230 of fig. 4A) similarly results in convergence of traces 34 and 35 associated with the constant current condition of fig. 3B. This is illustrated using corresponding traces 134 and 135 in fig. 4B, which indicate improved accuracy of the estimate of the regressive R-ohm.
As an example application, the battery pack 13 of fig. 1 may operate at a constant baseline current, with the regressive R-ohm value monotonically increasing as shown in fig. 3A. When the controller 50 of fig. 1 has determined based on a predetermined condition indicative of insufficient frequency content in the baseline current, the controller 50 requests injection of a time-varying oscillation 44 (fig. 5). By way of non-limiting illustration, if a constant charge current of 58 amps is provided to the battery pack 13 and a predetermined condition is detected or there is an indication of insufficient frequency content in the charge current, the controller 50 may respond by requesting injection of a time-varying oscillation 44 from the current control logic 55 of fig. 1, thereby causing the charge current to vary substantially from 58 amps by oscillating between 48 amps and 58 amps, such as at a frequency of 1 Hz. A sensor.
The predetermined conditions for triggering frequency content boosting may depend to some extent on the particular formula used to implement the BSE logic 52. For example, a timer of the controller 50 may be started at the beginning of a constant baseline current, where a threshold elapsed time is used as the predetermined condition. Other embodiments of the predetermined condition may include a threshold change in the baseline current, such as a current variance calculated across a time window, a cruise control system state, a plug-in state of charge, a threshold change in temperature, a SOC and/or a voltage of the battery pack 13, and so forth.
With respect to covariance, as will be understood by those skilled in the art, the kalman formula provides covariance or an approximation thereof. An extended kalman filter or other kalman formula using the BSE logic 52 may use the magnitude of the covariance as the predetermined condition. For example, the particle filter tracks the statistical distribution through random sampling of the associated model (e.g., equivalent circuit model 54 of FIG. 1). As a suitable trigger for selectively injecting the constant baseline current 42 of fig. 7 with additional frequency content of the current oscillation 44, statistical analysis may be performed with parametric fitting techniques, for example, via analytical formulae or by brute force methods. Using the "brute force" approach, the measurement signal may be acquired to perturb the sensor 16 of fig. 1 with noise at the level of accuracy, and then the relevant battery parameters recalculated. By repeating such a process a number of times, a cloud of parameter values indicative of the covariance is generated. The Sigma point kalman filter calculates the covariance from only a few selected disturbance points by comparison, while the extended kalman filter uses the partial derivatives of the single point and equivalent circuit model 54.
In a general sense, a signal-to-noise ratio (SNR) is used to inform injection trigger decisions. In the example extended kalman filter embodiment of the BSE logic 52, a good measure of SNR would be to compare an estimate of the battery parameters with an estimate of its standard deviation. If it is notxIs a column vector of the battery parameters,the extended kalman filter produces an estimate of the value,x * and the covariance matrix is then calculated,
Figure 453346DEST_PATH_IMAGE002
wherein
Figure 233083DEST_PATH_IMAGE003
Is a desired value. Then, fori th The parameters of the battery are set to be,
Figure 628292DEST_PATH_IMAGE004
is a measure of how accurately the extended kalman filter considers it to measure the parameter. For example, in the case of an ohmic resistance,kcan be used as an indicator of R-ohm. Taking the reciprocal of
Figure 442665DEST_PATH_IMAGE005
Beyond a certain value, we can start current oscillation, where
Figure 265127DEST_PATH_IMAGE006
The standard deviation, i.e. the square root of the variance, is indicated. Similarly, whenever other battery parameters lose accuracy on similar criteria, the controller 50 may decide to inject the time-varying current oscillation 44 depending on the context.
The controller 50 is provided with a coarse value of the battery parameter from the calibration that can be used in place of the estimate, which, as noted herein, can become unreliable. The calibration values are typically stored in a table, for example, where R-ohms are stored in a table indexed by% SOC and temperature. Suppose that
Figure 469843DEST_PATH_IMAGE007
As a parameterx i The controller 50 may set the value of
Figure 668744DEST_PATH_IMAGE008
At which value the injection of a time-varying current oscillation is triggered. The EKF can be implemented in the form of a square root, which in this case gives a matrixSSo that
Figure 212989DEST_PATH_IMAGE009
. Thus, in some embodiments, controller 50 may be selected fromSComputingC ii . Instead of a ratio, the controller 50 may alternatively be at
Figure 144035DEST_PATH_IMAGE010
Is triggered.
In the equivalent circuit model 54 of fig. 1, the ohmic resistance is a higher frequency impedance. At a representative sampling rate of 100ms (i.e., 10 Hz), the regressive R-ohm corresponds to the impedance of the battery pack 13 at frequencies above about 1 Hz. For example, if the baseline current 42 does not have a frequency content above 0.1Hz, there is a lack of information that can be used to accurately estimate R-ohm, causing the estimate to become unreliable and drift. Other battery parameters model lower frequency effects and therefore do not require as high a frequency content to adapt. However, if the frequency content of the baseline current is very low, the lower frequency battery parameters may similarly drift. Without sufficient excitation, the uncertainty in a given battery parameter grows over time. Thus, the predetermined conditions within the scope of the present disclosure may be extended to the above and other criteria for determining that too much time has elapsed since a significant current oscillation, which in turn would trigger a request for the current oscillation 44 of FIG. 7.
FIG. 7, best understood in conjunction with FIG. 5, is a timing diagram 45 schematically depicting the resulting current (I) of FIG. 5 in amperes (A)F) 46 with time-varying current oscillations 44 injected intermittently. As explained above, the final current 46 is the sum of the constant baseline current 42 and the current oscillation 44, and thus the constant baseline current 42 is equal to the final current 46 of fig. 5 when the current oscillation 44 does not continue.
From t0Beginning by receiving or delivering a constant baseline current 42 via the battery pack 13 shown in fig. 1, the controller 50 begins at about t1The current oscillation 44 is selectively requested to be injected into the constant baseline current 42 in response to predetermined conditions, a number of options are presented above with respect to the predetermined conditions. The injection continues until t2. Can be used by the controller 50The predetermined conditions make a decision as to when the injection of a given current oscillation 44 begins and continues precisely, some examples of which are set forth below. The controller 50 of fig. 1 estimates the ohmic resistance, impedance, and OCV of the battery pack 13 and/or other battery parameters via BSE logic 52. The estimation occurs simultaneously with the injection of the current oscillation 44 and also at other times. Thereafter, the controller 50 may estimate the current state of the battery pack 13 using the estimated battery parameters.
As depicted in fig. 7, for a representative charging cycle, constant current charging begins at t during the charging cycle0And continues until t7And wherein constant voltage charging starts at t7And continues until t8(e.g., trace 142) the injection of selective current oscillation 44 need not continue. In the illustrated embodiment, for example, the controller 50 is at t1And t2、t3And t4And t5And t6Requesting injection current oscillation 44. The frequency content of the current oscillation 44 may be the same in each injection case, or it may be different as shown.
There are various embodiments of suitable predetermined conditions for triggering the injection of the current oscillation 44, which embodiments may depend on the formula of the BSE logic 52, as described above. By way of example and not limitation, the predetermined condition may include a covariance or estimated error value from the BSE logic 52 that indicates a confidence in the estimated accuracy of the battery parameter. The predetermined condition may include a calibration duration in which the constant baseline current 42 is held constant prior to injection of the current oscillation 44, where such replacement relies on the use of a timer, such as a timer of the controller 50. Other values may be used as the predetermined/trigger condition, such as, but not limited to, a predetermined temperature difference, amp-hour time difference, and/or covariance of the battery pack 13.
Additionally, the offboard charging station 25 of FIG. 1 may have different configurations and capacities, which inform the controller 50 of the range of options provided for implementing the present teachings. For example, during a charging operation in which the offboard charging station 25 is connected to the battery pack 13/vehicle 10 and actively charges the battery pack 13, the controller 50 may request a particular constant charging current (e.g., 10 amps) for a short duration of time. Immediately following such a charging current may be a different constant charging current, for example 8 amps; followed by another 10 amp charge current and so on. In this illustrative example, the duration and possibly magnitude of each successive charging current may be selected by the controller 50 to generate the necessary frequency content for energizing the BSE logic 52.
The offboard charging station 25 may be configured as the smart charger 25S shown in fig. 1, i.e., a station programmed for, and therefore capable of communicating with, the controller 50 wirelessly or through hardwired transfer conductors to determine the charging demand and capacity of the battery pack 13. In such an embodiment, controller 50 may request that current oscillation 44 be injected into constant baseline current 42 by communicating a charging request to smart charger 25S, where smart charger 25S detects the demand of battery pack 13 with respect to final current 46. In such embodiments, the smart charger 25S responds by delivering a final current 46 to the battery pack 13 as the charging current, where the composition of the final current 46 at a given moment is either the constant current 42 alone or a combination of the constant current 42 and the time-varying current oscillation 44.
As yet another example, the battery pack 13 may receive or output a constant current, such as 10A. To provide the desired frequency content, the controller 50 may selectively discharge 1-2A of current, such as by selectively activating a resistive load or resident electrical components of the vehicle 10. The particular load may vary depending on the application, and thus may range from a sufficiently high current device, such as a battery or RESS heater, an air conditioning compressor, and the like. In this embodiment, the selective discharge of the battery pack 13 may occur during an active driving state of the vehicle 10, such as while cruising at a constant speed, or during an active charging state of the vehicle 10.
The method 100 set forth above is therefore intended to improve the accuracy of state and parameter estimation of a typical battery state estimator. The output of the battery state estimator will tend to grow with higher frequency content in its input. However, the low frequency content results in a reduced output, such as in the case of a DC charging current or other current variation less than 0.01 Hz. If the output has not exceeded the given threshold for too long, the controller 50 may request injection of the current oscillation 44 described above. For parameters associated with lower frequency effects, some embodiments may use separate triggers or predetermined conditions, each having its own time constant. In essence, the present approach selectively adds sufficient frequency content to ensure that a given signal rises above the associated noise level, and thus addresses the vulnerability in conventional BSE approaches used with motor vehicles and other systems having the battery pack 13 described above. These and other advantages will be readily appreciated by those of ordinary skill in the art based on the foregoing disclosure.
While the best modes and some of the other embodiments have been described in detail, there are numerous alternative designs and embodiments for practicing the present teachings as defined in the appended claims. Those skilled in the art will recognize modifications to the disclosed embodiments without departing from the scope of the present disclosure. Moreover, the present concepts expressly include combinations and subcombinations of the described elements and features. The detailed description and drawings are a support and description for the present teachings, while the scope of the present teachings is limited only by the claims.

Claims (10)

1. A method for estimating a current state of a battery pack using a controller having Battery State Estimator (BSE) logic, the method comprising:
receiving or delivering a constant baseline current via the battery pack;
selectively requesting, via the controller, injection of a current oscillation having a time-varying frequency content into the constant baseline current in response to a predetermined condition, wherein the constant baseline current and the current oscillation combine to form a final current;
estimating battery parameters of the battery pack via the BSE logic concurrently with the current oscillation to thereby generate estimated battery parameters; and is
Estimating, via the controller, the current state of the battery pack using the estimated battery parameters to thereby generate an estimated battery state of the battery pack.
2. The method of claim 1, wherein the BSE logic comprises a kalman filter.
3. The method of claim 1, further comprising: controlling, via the controller, power flow to or from the battery pack using the estimated battery state.
4. The method of claim 3, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises requesting, via the controller, a constant charging current from an offboard charging station as a constant baseline current; and wherein controlling power flow to or from the battery pack comprises charging the battery pack using the final current.
5. The method of claim 3, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises selectively controlling an on/off state of an electrical load connected to the battery pack while receiving or delivering the constant baseline current to thereby generate the current oscillation, and wherein controlling power flow to or from the battery pack comprises discharging the battery pack to the electrical load.
6. The method of claim 3, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises selectively requesting a series of constant charging currents from an offboard charging station, each of the constant charging currents having a different frequency content, to thereby generate the current oscillation, and wherein controlling power flow to or from the battery pack using the estimated battery state comprises charging the battery pack using the final current.
7. The method of claim 1, wherein selectively requesting injection of the current oscillation into the constant baseline current comprises communicating a charge request from the controller to an off-board smart charger configured to detect a demand of the battery pack for the final current and to transmit the final current as a charge current to the battery pack.
8. The method of claim 1, wherein estimating the battery parameters comprises estimating an ohmic resistance, an impedance, and/or an open circuit voltage of the battery pack.
9. The method of claim 1, wherein the frequency of the current oscillation is less than about 1Hz, and the constant baseline current has a frequency less than about 0.01 Hz.
10. An electrical system, comprising:
a battery pack;
a rotating electrical machine electrically connected to and driven by the battery pack; and
a controller configured to estimate a current state of a battery pack using Battery State Estimator (BSE) logic, wherein the controller is configured to:
determining, via the BSE logic, a frequency content of a constant baseline current delivered to or from the battery pack, wherein the constant baseline current has a frequency of less than about 0.01 Hz;
selectively requesting injection of a current oscillation into the constant baseline current in response to a predetermined condition, wherein the constant baseline current and the current oscillation combine to form a final current, and wherein the current oscillation has a frequency in a range between about 0.1Hz and 1 Hz;
estimating battery parameters of the battery pack via the BSE logic concurrently with the current oscillation to thereby generate estimated battery parameters, wherein the estimated battery parameters are an ohmic resistance, an impedance, and/or an open circuit voltage of the battery pack;
estimating the current state of the battery pack using the estimated battery parameters as estimated battery states; and is
Using the estimated battery state to control power flow from the electric machine to the battery pack or from the battery pack to the electric machine, respectively.
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